where \(\mathcal {L}_{GP}=\mathbb {E}_{\mathbf {\hat{I}}}||\triangledown _{\mathbf {\hat{I}}}D(\mathbf {\hat{I}})||^2\)  is the gradient penalty [1]} and \(\lambda _{GP}=1e-3\) . We also use the feature match loss [2]} \(\mathcal {L}_{fm}\) , which is based on L1 loss between discriminator features of true and fake samples. \(\mathcal {L}_{fm}\)  is usually used to stable the GAN training. It can also slightly improve the performance.
Furthermore, we use the HRF loss \(\mathcal {L}_{hrf}\)  in [3]} as
\(\mathcal {L}_{hrf}=\mathbb {E}(\left[\phi _{hrf}(\mathbf {\hat{I}})-\phi _{hrf}(\mathbf {\tilde{I}})\right]^2),\) 
